ArticleThe journal of physical chemistry letters2022
BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations.
Article in The journal of physical chemistry letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Buffer Region Embedding for Hybrid Machine-Learned/Molecular-Mechanical Simulations in Complex Environments.Journal of chemical information and modeling · 2026Article
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Structure making and breaking in electrolyte solutions explained by water energetics.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.Digital discovery · 2025Article
- Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications.The journal of physical chemistry. B · 2025Article
- High-performance QM/MM Enhanced Sampling Molecular Dynamics Simulations with GENESIS SPDYN and QSimulate-QM.Journal of chemical theory and computation · 2025Article
- Efficient Training of Neural Network Potentials for Chemical and Enzymatic Reactions by Continual Learning.Journal of chemical theory and computation · 2025Article
- A Robust and Versatile QM/MM Interface for Molecular Dynamics in GROMOS.Journal of computational chemistry · 2025Article
- Alchemical Free-Energy Calculations at Quantum-Chemical Precision.The journal of physical chemistry letters · 2025Article
- Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.Journal of chemical theory and computation · 2025Article
- Article
- CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.The journal of physical chemistry. B · 2024Review
- Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.The journal of physical chemistry. B · 2024Article
- Article
- Training machine learning potentials for reactive systems: A Colab tutorial on basic models.Journal of computational chemistry · 2024Article
- Implementation and Validation of an OpenMM Plugin for the Deep Potential Representation of Potential Energy.International journal of molecular sciences · 2024Article
- Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects.Chemical science · 2023Article
- Electrostatic Embedding of Machine Learning Potentials.Journal of chemical theory and computation · 2023Article
- Machine Learning Interatomic Potentials and Long-Range Physics.The journal of physical chemistry. A · 2023Review
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Authors and funding
5 authors.
Funding
Abstract
Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artifacts at the interface. Furthermore, they are still limited by high computational costs of quantum chemical calculations. In this work, we develop the buffer region neural network (BuRNN), an alternative approach to existing QM/MM schemes, which introduces a buffer region that experiences full electronic polarization by the inner QM region to minimize artifacts. The interactions between the QM and the buffer region are described by deep neural networks (NNs), which leads to the high computational efficiency of this hybrid NN/MM scheme while retaining quantum chemical accuracy. We demonstrate the BuRNN approach by performing NN/MM simulations of the hexa-aqua iron complex.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.